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Wip, CUDA porting malloc improvements, gpu accel for non-llama, backport old quants

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This commit is contained in:
Concedo 2023-06-28 18:20:46 +08:00
parent 9527a783ea
commit b4698abafc
10 changed files with 842 additions and 24 deletions
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10
CMakeLists.txt
View file

@ -1,5 +1,5 @@
# DO NOT USE THIS FILE.
# IT'S ONLY FOR CUBLAS BUILD PURPOSES ON WINDOWS VISUAL STUDIO.
# DO NOT USE THIS FILE.
# IT'S ONLY FOR CUBLAS BUILD PURPOSES ON WINDOWS VISUAL STUDIO.
# IT WILL NOT BE UPDATED OR MAINTAINED !!!
message(STATUS "============== ============== ==============")
@ -69,6 +69,7 @@ if (LLAMA_CUBLAS)
set(GGML_CUDA_SOURCES ggml-cuda.cu ggml-cuda.h)
set(GGML_V2_CUDA_SOURCES otherarch/ggml_v2-cuda.cu otherarch/ggml_v2-cuda.h)
set(GGML_V2_LEGACY_CUDA_SOURCES otherarch/ggml_v2-cuda-legacy.cu otherarch/ggml_v2-cuda-legacy.h)
add_compile_definitions(GGML_USE_CUBLAS)
@ -259,7 +260,8 @@ set_target_properties(ggml_v1 PROPERTIES POSITION_INDEPENDENT_CODE ON)
add_library(ggml_v2 OBJECT
otherarch/ggml_v2.c
otherarch/ggml_v2.h
${GGML_V2_CUDA_SOURCES})
${GGML_V2_CUDA_SOURCES}
${GGML_V2_LEGACY_CUDA_SOURCES})
target_include_directories(ggml_v2 PUBLIC . ./otherarch ./otherarch/tools)
target_compile_features(ggml_v2 PUBLIC c_std_11) # don't bump
target_link_libraries(ggml_v2 PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS})
@ -273,7 +275,7 @@ target_compile_features(common2 PUBLIC cxx_std_11) # don't bump
target_link_libraries(common2 PRIVATE ggml ${LLAMA_EXTRA_LIBS})
set_target_properties(common2 PROPERTIES POSITION_INDEPENDENT_CODE ON)
add_library(gpttype_adapter
add_library(gpttype_adapter
gpttype_adapter.cpp)
target_include_directories(gpttype_adapter PUBLIC . ./otherarch ./otherarch/tools ./examples)
target_compile_features(gpttype_adapter PUBLIC cxx_std_11) # don't bump

4
Makefile
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@ -136,7 +136,7 @@ ifdef LLAMA_CUBLAS
CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
CXXFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
OBJS += ggml-cuda.o ggml_v2-cuda.o
OBJS += ggml-cuda.o ggml_v2-cuda.o ggml_v2-cuda-legacy.o
NVCC = nvcc
NVCCFLAGS = --forward-unknown-to-host-compiler -arch=native
ifdef LLAMA_CUDA_DMMV_X
@ -161,6 +161,8 @@ ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) $(CUBLAS_CXXFLAGS) -Wno-pedantic -c $< -o $@
ggml_v2-cuda.o: otherarch/ggml_v2-cuda.cu otherarch/ggml_v2-cuda.h
$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) $(CUBLAS_CXXFLAGS) -Wno-pedantic -c $< -o $@
ggml_v2-cuda-legacy.o: otherarch/ggml_v2-cuda-legacy.cu otherarch/ggml_v2-cuda-legacy.h
$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) $(CUBLAS_CXXFLAGS) -Wno-pedantic -c $< -o $@
endif # LLAMA_CUBLAS
ifdef LLAMA_METAL

37
ggml-cuda.cu
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@ -1763,15 +1763,40 @@ static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
int id;
CUDA_CHECK(cudaGetDevice(&id));
int best_i = -1;
size_t best_size = std::numeric_limits<size_t>::max(); //smallest unused buffer that fits our needs
int worst_i = -1;
size_t worst_size = 0; //largest unused buffer seen so far
for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
cuda_buffer& b = g_cuda_buffer_pool[id][i];
if (b.size >= size && b.ptr != nullptr) {
void * ptr = b.ptr;
*actual_size = b.size;
b.ptr = nullptr;
b.size = 0;
return ptr;
if (b.size > 0 && b.size >= size && b.size < best_size)
{
best_i = i;
best_size = b.size;
}
if (b.size > 0 && b.size > worst_size)
{
worst_i = i;
worst_size = b.size;
}
}
if(best_i!=-1) //found the smallest buffer that fits our needs
{
cuda_buffer& b = g_cuda_buffer_pool[id][best_i];
void * ptr = b.ptr;
*actual_size = b.size;
b.ptr = nullptr;
b.size = 0;
return ptr;
}
if(worst_i!=-1) //no buffer that fits our needs, resize largest one to save memory
{
cuda_buffer& b = g_cuda_buffer_pool[id][worst_i];
b.size = 0;
void * ptr = b.ptr;
cudaFree(ptr);
b.ptr = ptr = nullptr;
}
void * ptr;
CUDA_CHECK(cudaMalloc((void **) &ptr, size));

711
otherarch/ggml_v2-cuda-legacy.cu Normal file
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@ -0,0 +1,711 @@
#include <cstddef>
#include <cstdint>
#include <stdint.h>
#include <stdio.h>
#include <atomic>
#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <cuda_fp16.h>
#include "ggml_v2-cuda-legacy.h"
#include "ggml_v2.h"
static_assert(sizeof(half) == sizeof(ggml_v2_fp16_t), "wrong fp16 size");
#define CUDA_CHECK(err) \
do { \
cudaError_t err_ = (err); \
if (err_ != cudaSuccess) { \
fprintf(stderr, "CUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \
cudaGetErrorString(err_)); \
exit(1); \
} \
} while (0)
#define CUBLAS_CHECK(err) \
do { \
cublasStatus_t err_ = (err); \
if (err_ != CUBLAS_STATUS_SUCCESS) { \
fprintf(stderr, "cuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__); \
exit(1); \
} \
} while (0)
typedef void (*to_fp32_cuda_t)(const void * x, float * y, int k, cudaStream_t stream);
#define QK4_0 32
typedef struct {
float d; // delta
uint8_t qs[QK4_0 / 2]; // nibbles / quants
} block_q4_0;
static_assert(sizeof(block_q4_0) == sizeof(float) + QK4_0 / 2, "wrong q4_0 block size/padding");
#define QK4_1 32
typedef struct {
float d; // delta
float m; // min
uint8_t qs[QK4_1 / 2]; // nibbles / quants
} block_q4_1;
static_assert(sizeof(block_q4_1) == sizeof(float) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
#define QK4_2 16
typedef struct {
half d; // delta
uint8_t qs[QK4_2 / 2]; // nibbles / quants
} block_q4_2;
static_assert(sizeof(block_q4_2) == sizeof(ggml_v2_fp16_t) + QK4_2 / 2, "wrong q4_2 block size/padding");
#define QK4_3 16
typedef struct {
__half d; // delta
__half m; // min
uint8_t qs[QK4_3 / 2]; // nibbles / quants
} block_q4_3;
static_assert(sizeof(block_q4_3) == 2 * sizeof(ggml_fp16_t) + QK4_3 / 2, "wrong q4_3 block size/padding");
#define QK5_0 32
typedef struct {
half d; // delta
uint8_t qh[4]; // 5-th bit of quants
uint8_t qs[QK5_0 / 2]; // nibbles / quants
} block_q5_0;
static_assert(sizeof(block_q5_0) == sizeof(ggml_v2_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
#define QK5_1 32
typedef struct {
half d; // delta
half m; // min
uint8_t qh[4]; // 5-th bit of quants
uint8_t qs[QK5_1 / 2]; // nibbles / quants
} block_q5_1;
static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_v2_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
#define QK8_0 32
typedef struct {
float d; // delta
int8_t qs[QK8_0]; // quants
} block_q8_0;
static_assert(sizeof(block_q8_0) == sizeof(float) + QK8_0, "wrong q8_0 block size/padding");
static __global__ void dequantize_block_q4_0(const void * vx, float * y) {
const block_q4_0 * x = (const block_q4_0 *) vx;
const int i = blockIdx.x;
const float d = x[i].d;
const uint8_t * pp = x[i].qs;
for (int l = 0; l < QK4_0; l += 2) {
const uint8_t vi = pp[l/2];
const int8_t vi0 = vi & 0xf;
const int8_t vi1 = vi >> 4;
const float v0 = (vi0 - 8)*d;
const float v1 = (vi1 - 8)*d;
y[i*QK4_0 + l + 0] = v0;
y[i*QK4_0 + l + 1] = v1;
}
}
static __global__ void dequantize_block_q4_1(const void * vx, float * y) {
const block_q4_1 * x = (const block_q4_1 *) vx;
const int i = blockIdx.x;
const float d = x[i].d;
const float m = x[i].m;
const uint8_t * pp = x[i].qs;
for (int l = 0; l < QK4_1; l += 2) {
const uint8_t vi = pp[l/2];
const int8_t vi0 = vi & 0xf;
const int8_t vi1 = vi >> 4;
const float v0 = vi0*d + m;
const float v1 = vi1*d + m;
y[i*QK4_1 + l + 0] = v0;
y[i*QK4_1 + l + 1] = v1;
}
}
static __global__ void dequantize_block_q4_2(const void * vx, float * y) {
const block_q4_2 * x = (const block_q4_2 *) vx;
const int i = blockIdx.x;
const float d = x[i].d;
const uint8_t * pp = x[i].qs;
for (int l = 0; l < QK4_2; l += 2) {
const uint8_t vi = pp[l/2];
const int8_t vi0 = vi & 0xf;
const int8_t vi1 = vi >> 4;
const float v0 = (vi0 - 8)*d;
const float v1 = (vi1 - 8)*d;
y[i*QK4_2 + l + 0] = v0;
y[i*QK4_2 + l + 1] = v1;
}
}
static __global__ void dequantize_block_q4_3(const void * vx, float * y) {
const block_q4_3 * x = (const block_q4_3 *) vx;
const int i = blockIdx.x;
const float d = x[i].d;
const float m = x[i].m;
const uint8_t * pp = x[i].qs;
for (int l = 0; l < QK4_3; l += 2) {
const uint8_t vi = pp[l/2];
const int8_t vi0 = vi & 0xf;
const int8_t vi1 = vi >> 4;
const float v0 = vi0*d + m;
const float v1 = vi1*d + m;
y[i*QK4_3 + l + 0] = v0;
y[i*QK4_3 + l + 1] = v1;
}
}
static __global__ void dequantize_block_q5_0(const void * vx, float * y) {
const block_q5_0 * x = (const block_q5_0 *) vx;
const int i = blockIdx.x;
const float d = x[i].d;
const uint8_t * pp = x[i].qs;
uint32_t qh;
memcpy(&qh, x[i].qh, sizeof(qh));
for (int l = 0; l < QK5_0; l += 2) {
const uint8_t vi = pp[l/2];
const int8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
const int8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
const int8_t vi0 = ((vi & 0xf) | vh0);
const int8_t vi1 = ((vi >> 4) | vh1);
const float v0 = (vi0 - 16)*d;
const float v1 = (vi1 - 16)*d;
y[i*QK5_0 + l + 0] = v0;
y[i*QK5_0 + l + 1] = v1;
}
}
static __global__ void dequantize_block_q5_1(const void * vx, float * y) {
const block_q5_1 * x = (const block_q5_1 *) vx;
const int i = blockIdx.x;
const float d = x[i].d;
const float m = x[i].m;
const uint8_t * pp = x[i].qs;
uint32_t qh;
memcpy(&qh, x[i].qh, sizeof(qh));
for (int l = 0; l < QK5_1; l += 2) {
const uint8_t vi = pp[l/2];
const int8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
const int8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
const int8_t vi0 = (vi & 0xf) | vh0;
const int8_t vi1 = (vi >> 4) | vh1;
const float v0 = vi0*d + m;
const float v1 = vi1*d + m;
y[i*QK5_1 + l + 0] = v0;
y[i*QK5_1 + l + 1] = v1;
}
}
static __global__ void dequantize_block_q8_0(const void * vx, float * y) {
const block_q8_0 * x = (const block_q8_0 *) vx;
const int i = blockIdx.x;
const float d = x[i].d;
const int8_t * pp = x[i].qs;
for (int l = 0; l < QK8_0; l++) {
const int8_t vi = pp[l];
y[i*QK8_0 + l] = vi*d;
}
}
static void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_0;
dequantize_block_q4_0<<<nb, 1, 0, stream>>>(vx, y);
}
static void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_1;
dequantize_block_q4_1<<<nb, 1, 0, stream>>>(vx, y);
}
static void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_2;
dequantize_block_q4_2<<<nb, 1, 0, stream>>>(vx, y);
}
void dequantize_row_q4_3_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_3;
dequantize_block_q4_3<<<nb, 1, 0, stream>>>(vx, y);
}
static void dequantize_row_q5_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK5_0;
dequantize_block_q5_0<<<nb, 1, 0, stream>>>(vx, y);
}
static void dequantize_row_q5_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK5_1;
dequantize_block_q5_1<<<nb, 1, 0, stream>>>(vx, y);
}
static void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK8_0;
dequantize_block_q8_0<<<nb, 1, 0, stream>>>(vx, y);
}
// TODO: optimize
static __global__ void convert_fp16_to_fp32(const void * vx, float * y) {
const half * x = (const half *) vx;
const int i = blockIdx.x;
y[i] = __half2float(x[i]);
}
static void convert_fp16_to_fp32_cuda(const void * x, float * y, int k, cudaStream_t stream) {
convert_fp16_to_fp32<<<k, 1, 0, stream>>>(x, y);
}
static to_fp32_cuda_t ggml_v2_get_to_fp32_cuda(ggml_v2_type type) {
switch (type) {
case GGML_V2_TYPE_Q4_0:
return dequantize_row_q4_0_cuda;
case GGML_V2_TYPE_Q4_1:
return dequantize_row_q4_1_cuda;
case GGML_V2_TYPE_Q4_2:
return dequantize_row_q4_2_cuda;
case GGML_V2_TYPE_Q4_3:
return dequantize_row_q4_3_cuda;
case GGML_V2_TYPE_Q5_0:
return dequantize_row_q5_0_cuda;
case GGML_V2_TYPE_Q5_1:
return dequantize_row_q5_1_cuda;
case GGML_V2_TYPE_Q8_0:
return dequantize_row_q8_0_cuda;
case GGML_V2_TYPE_F16:
return convert_fp16_to_fp32_cuda;
default:
return nullptr;
}
}
// buffer pool for cuda
#define MAX_CUDA_BUFFERS 16
struct scoped_spin_lock {
std::atomic_flag& lock;
scoped_spin_lock(std::atomic_flag& lock) : lock(lock) {
while (lock.test_and_set(std::memory_order_acquire)) {
; // spin
}
}
~scoped_spin_lock() {
lock.clear(std::memory_order_release);
}
scoped_spin_lock(const scoped_spin_lock&) = delete;
scoped_spin_lock& operator=(const scoped_spin_lock&) = delete;
};
struct cuda_buffer {
void * ptr = nullptr;
size_t size = 0;
};
static cuda_buffer g_cuda_buffer_pool[MAX_CUDA_BUFFERS];
static std::atomic_flag g_cuda_pool_lock = ATOMIC_FLAG_INIT;
static void * ggml_v2_cuda_pool_malloc(size_t size, size_t * actual_size) {
scoped_spin_lock lock(g_cuda_pool_lock);
for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
cuda_buffer& b = g_cuda_buffer_pool[i];
if (b.size >= size && b.ptr != nullptr) {
void * ptr = b.ptr;
*actual_size = b.size;
b.ptr = nullptr;
b.size = 0;
return ptr;
}
}
void * ptr;
CUDA_CHECK(cudaMalloc((void **) &ptr, size));
*actual_size = size;
return ptr;
}
static void ggml_v2_cuda_pool_free(void * ptr, size_t size) {
scoped_spin_lock lock(g_cuda_pool_lock);
for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
cuda_buffer& b = g_cuda_buffer_pool[i];
if (b.ptr == nullptr) {
b.ptr = ptr;
b.size = size;
return;
}
}
fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n");
CUDA_CHECK(cudaFree(ptr));
}
#define GGML_V2_CUDA_MAX_STREAMS 8 // Set this to 1 for reproducible matrix multiplication.
#define GGML_V2_CUDA_MAX_EVENTS 64
static cublasHandle_t g_cublasH = nullptr;
static cudaStream_t g_cudaStreams[GGML_V2_CUDA_MAX_STREAMS] = { nullptr };
static cudaStream_t g_cudaStreams2[GGML_V2_CUDA_MAX_STREAMS] = { nullptr };
static cudaEvent_t g_cudaEvents[GGML_V2_CUDA_MAX_EVENTS] = { nullptr };
void ggml_v2_init_cublas_legacy() {
if (g_cublasH == nullptr) {
// create streams
for (int i = 0; i < GGML_V2_CUDA_MAX_STREAMS; ++i) {
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams[i], cudaStreamNonBlocking));
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams2[i], cudaStreamNonBlocking));
}
// create events
for (int i = 0; i < GGML_V2_CUDA_MAX_EVENTS; ++i) {
CUDA_CHECK(cudaEventCreateWithFlags(&g_cudaEvents[i], cudaEventDisableTiming));
}
// create cublas handle
CUBLAS_CHECK(cublasCreate(&g_cublasH));
CUBLAS_CHECK(cublasSetMathMode(g_cublasH, CUBLAS_TF32_TENSOR_OP_MATH));
// configure logging to stdout
// CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr));
}
}
static cudaError_t ggml_v2_cuda_h2d_tensor_2d(void * dst, const struct ggml_v2_tensor * src, uint64_t i3, uint64_t i2, cudaStream_t stream) {
const uint64_t ne0 = src->ne[0];
const uint64_t ne1 = src->ne[1];
const uint64_t nb0 = src->nb[0];
const uint64_t nb1 = src->nb[1];
const uint64_t nb2 = src->nb[2];
const uint64_t nb3 = src->nb[3];
const enum ggml_v2_type type = src->type;
const size_t ts = ggml_v2_type_size(type);
const size_t bs = ggml_v2_blck_size(type);
const void * x = (const void *) ((const char *) src->data + i2*nb2 + i3*nb3);
if (nb0 == ts && nb1 == ts*ne0/bs) {
return cudaMemcpyAsync(dst, x, ne1*nb1, cudaMemcpyHostToDevice, stream);
} else if (nb0 == ts) {
return cudaMemcpy2DAsync(dst, ts*ne0/bs, x, nb1, ts*ne0/bs, ne1, cudaMemcpyHostToDevice, stream);
} else {
for (uint64_t i1 = 0; i1 < ne1; i1++) {
const void * rx = (const void *) ((const char *) x + i1*nb1);
void * rd = (void *) ((char *) dst + i1*ts*ne0/bs);
// pretend the row is a matrix with cols=1
cudaError_t r = cudaMemcpy2DAsync(rd, ts/bs, rx, nb0, ts/bs, ne0, cudaMemcpyHostToDevice, stream);
if (r != cudaSuccess) return r;
}
return cudaSuccess;
}
}
static void ggml_v2_cuda_mul_mat_f32(const ggml_v2_tensor * src0, const ggml_v2_tensor * src1, ggml_v2_tensor * dst) {
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne00;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
const int n_mm = ne03 * ne02;
size_t x_size, y_size, d_size;
float * d_X = (float *) ggml_v2_cuda_pool_malloc(n_mm * sizeof(float) * x_ne, &x_size);
float * d_Y = (float *) ggml_v2_cuda_pool_malloc(n_mm * sizeof(float) * y_ne, &y_size);
float * d_D = (float *) ggml_v2_cuda_pool_malloc(n_mm * sizeof(float) * d_ne, &d_size);
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
int i = i03*ne02 + i02;
cudaStream_t cudaStream = g_cudaStreams[i % GGML_V2_CUDA_MAX_STREAMS];
float * c_X = d_X + i * x_ne;
float * c_Y = d_Y + i * y_ne;
float * c_D = d_D + i * d_ne;
// copy data to device
CUDA_CHECK(ggml_v2_cuda_h2d_tensor_2d(c_X, src0, i03, i02, cudaStream));
CUDA_CHECK(ggml_v2_cuda_h2d_tensor_2d(c_Y, src1, i03, i02, cudaStream));
// compute
CUBLAS_CHECK(cublasSetStream(g_cublasH, cudaStream));
CUBLAS_CHECK(
cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha, c_X, ne00,
c_Y, ne10,
&beta, c_D, ne01));
// copy dst to host
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
CUDA_CHECK(cudaMemcpyAsync(d, c_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
}
}
CUDA_CHECK(cudaDeviceSynchronize());
ggml_v2_cuda_pool_free(d_X, x_size);
ggml_v2_cuda_pool_free(d_Y, y_size);
ggml_v2_cuda_pool_free(d_D, d_size);
}
static void ggml_v2_cuda_mul_mat_f16(const ggml_v2_tensor * src0, const ggml_v2_tensor * src1, ggml_v2_tensor * dst, void * wdata, size_t /* wsize */) {
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int nb10 = src1->nb[0];
const int nb11 = src1->nb[1];
const int nb12 = src1->nb[2];
const int nb13 = src1->nb[3];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne00;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
const int n_mm = ne03 * ne02;
size_t x_size, y_size, d_size;
half * d_X = (half *) ggml_v2_cuda_pool_malloc(n_mm * sizeof(half) * x_ne, &x_size);
half * d_Y = (half *) ggml_v2_cuda_pool_malloc(n_mm * sizeof(half) * y_ne, &y_size);
float * d_D = (float *) ggml_v2_cuda_pool_malloc(n_mm * sizeof(float) * d_ne, &d_size);
bool src1_cont_rows = nb10 == sizeof(float);
bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float);
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
int i = i03*ne02 + i02;
cudaStream_t cudaStream = g_cudaStreams[i % GGML_V2_CUDA_MAX_STREAMS];
half * c_X = d_X + i * x_ne;
half * c_Y = d_Y + i * y_ne;
float * c_D = d_D + i * d_ne;
// copy src0 to device
CUDA_CHECK(ggml_v2_cuda_h2d_tensor_2d(c_X, src0, i03, i02, cudaStream));
// convert src1 to fp16
// TODO: use multiple threads
ggml_v2_fp16_t * const tmp = (ggml_v2_fp16_t *) wdata + (ne11 * ne10) * (i03 * ne02 + i02);
char * src1i = (char *) src1->data + i03*nb13 + i02*nb12;
if (src1_cont_rows) {
if (src1_cont_cols) {
ggml_v2_fp32_to_fp16_row((float *) src1i, tmp, ne10*ne11);
}
else {
for (int64_t i01 = 0; i01 < ne11; i01++) {
ggml_v2_fp32_to_fp16_row((float *) (src1i + i01*nb11), tmp + i01*ne10, ne10);
}
}
}
else {
for (int64_t i01 = 0; i01 < ne11; i01++) {
for (int64_t i00 = 0; i00 < ne10; i00++) {
// very slow due to no inlining
tmp[i01*ne10 + i00] = ggml_v2_fp32_to_fp16(*(float *) (src1i + i01*nb11 + i00*nb10));
}
}
}
// copy src1 to device
CUDA_CHECK(cudaMemcpyAsync(c_Y, tmp, sizeof(half) * y_ne, cudaMemcpyHostToDevice, cudaStream));
// compute
CUBLAS_CHECK(cublasSetStream(g_cublasH, cudaStream));
CUBLAS_CHECK(
cublasGemmEx(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha, c_X, CUDA_R_16F, ne00,
c_Y, CUDA_R_16F, ne10,
&beta, c_D, CUDA_R_32F, ne01,
CUBLAS_COMPUTE_32F_FAST_16F,
CUBLAS_GEMM_DEFAULT));
// copy dst to host
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
CUDA_CHECK(cudaMemcpyAsync(d, c_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
}
}
CUDA_CHECK(cudaDeviceSynchronize());
ggml_v2_cuda_pool_free(d_X, x_size);
ggml_v2_cuda_pool_free(d_Y, y_size);
ggml_v2_cuda_pool_free(d_D, d_size);
}
static void ggml_v2_cuda_mul_mat_q_f32(const ggml_v2_tensor * src0, const ggml_v2_tensor * src1, ggml_v2_tensor * dst) {
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
const ggml_v2_type type = src0->type;
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne00;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
const int n_mm = ne03 * ne02;
const size_t q_sz = ggml_v2_type_size(type) * x_ne / ggml_v2_blck_size(type);
size_t x_size, y_size, d_size, q_size;
float * d_X = (float *) ggml_v2_cuda_pool_malloc(n_mm * sizeof(float) * x_ne, &x_size);
float * d_Y = (float *) ggml_v2_cuda_pool_malloc(n_mm * sizeof(float) * y_ne, &y_size);
float * d_D = (float *) ggml_v2_cuda_pool_malloc(n_mm * sizeof(float) * d_ne, &d_size);
char * d_Q = (char *) ggml_v2_cuda_pool_malloc(n_mm * q_sz, &q_size);
const to_fp32_cuda_t to_fp32_cuda = ggml_v2_get_to_fp32_cuda(type);
GGML_V2_ASSERT(to_fp32_cuda != nullptr);
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
int i = i03*ne02 + i02;
cudaStream_t cudaStream = g_cudaStreams[i % GGML_V2_CUDA_MAX_STREAMS];
cudaStream_t cudaStream2 = g_cudaStreams2[i % GGML_V2_CUDA_MAX_STREAMS];
cudaEvent_t cudaEvent = g_cudaEvents[i % GGML_V2_CUDA_MAX_EVENTS];
float * c_X = d_X + i * x_ne;
float * c_Y = d_Y + i * y_ne;
float * c_D = d_D + i * d_ne;
char * c_Q = d_Q + i * q_sz;
// copy src0 and convert to fp32 on device
CUDA_CHECK(ggml_v2_cuda_h2d_tensor_2d(c_Q, src0, i03, i02, cudaStream2));
to_fp32_cuda(c_Q, c_X, x_ne, cudaStream2);
CUDA_CHECK(cudaGetLastError());
CUDA_CHECK(cudaEventRecord(cudaEvent, cudaStream2));
// copy src1 to device
CUDA_CHECK(ggml_v2_cuda_h2d_tensor_2d(c_Y, src1, i03, i02, cudaStream));
// wait for conversion
CUDA_CHECK(cudaStreamWaitEvent(cudaStream, cudaEvent, 0));
// compute
CUBLAS_CHECK(cublasSetStream(g_cublasH, cudaStream));
CUBLAS_CHECK(
cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha, c_X, ne00,
c_Y, ne10,
&beta, c_D, ne01));
// copy dst to host
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
CUDA_CHECK(cudaMemcpyAsync(d, c_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
}
}
CUDA_CHECK(cudaDeviceSynchronize());
ggml_v2_cuda_pool_free(d_X, x_size);
ggml_v2_cuda_pool_free(d_Y, y_size);
ggml_v2_cuda_pool_free(d_D, d_size);
ggml_v2_cuda_pool_free(d_Q, q_size);
}
bool ggml_v2_cuda_mul_mat_use_f16(const struct ggml_v2_tensor * src0, const struct ggml_v2_tensor * src1, struct ggml_v2_tensor * /* dst */) {
size_t src0_sz = ggml_v2_nbytes(src0);
size_t src1_sz = ggml_v2_nbytes(src1);
// mul_mat_q: src0 is converted to fp32 on device
size_t mul_mat_q_transfer = src0_sz + src1_sz;
// mul_mat_f16: src1 is converted to fp16 on cpu
size_t mul_mat_f16_transfer = src0_sz + sizeof(half) * ggml_v2_nelements(src1);
// choose the smaller one to transfer to the device
// TODO: this is not always the best choice due to the overhead of converting to fp16
return mul_mat_f16_transfer < mul_mat_q_transfer;
}
void ggml_v2_cuda_mul_mat_legacy(const ggml_v2_tensor * src0, const ggml_v2_tensor * src1, ggml_v2_tensor * dst, void * wdata, size_t wsize) {
GGML_V2_ASSERT(ggml_v2_cuda_can_mul_mat(src0, src1, dst));
if (src0->type == GGML_V2_TYPE_F32) {
ggml_v2_cuda_mul_mat_f32(src0, src1, dst);
}
else if (src0->type == GGML_V2_TYPE_F16) {
if (ggml_v2_cuda_mul_mat_use_f16(src0, src1, dst)) {
ggml_v2_cuda_mul_mat_f16(src0, src1, dst, wdata, wsize);
}
else {
ggml_v2_cuda_mul_mat_q_f32(src0, src1, dst);
}
}
else if (ggml_v2_is_quantized(src0->type)) {
ggml_v2_cuda_mul_mat_q_f32(src0, src1, dst);
}
else {
GGML_V2_ASSERT(false);
}
}

14
otherarch/ggml_v2-cuda-legacy.h Normal file
View file

@ -0,0 +1,14 @@
#include "ggml_v2.h"
#ifdef __cplusplus
extern "C" {
#endif
void ggml_v2_init_cublas_legacy(void);
void ggml_v2_cuda_mul_mat_legacy(const struct ggml_v2_tensor * src0, const struct ggml_v2_tensor * src1, struct ggml_v2_tensor * dst, void * wdata, size_t wsize);
#ifdef __cplusplus
}
#endif

32
otherarch/ggml_v2.c
View file

@ -141,6 +141,7 @@ inline static void* ggml_v2_aligned_malloc(size_t size) {
#include <cblas.h>
#elif defined(GGML_USE_CUBLAS)
#include "ggml_v2-cuda.h"
#include "ggml_v2-cuda-legacy.h"
#endif
#if defined(GGML_USE_CLBLAST)
#include "ggml_v2-opencl.h"
@ -1524,9 +1525,9 @@ quantize_fns_t2 ggml_v2_internal_get_quantize_fn(size_t i) {
bool quants_unshuffled = false; //new GGJT_2 is unshuffled, all old ones are shuffled
static const quantize_fns_t2 quantize_fns_v2[GGML_V2_TYPE_COUNT]; //forward decl
static inline quantize_fns_t2 get_quantize_fn(size_t i)
static inline quantize_fns_t2 get_quantize_fn(size_t i)
{
return(quants_unshuffled?quantize_fns[i]:quantize_fns_v2[i]);
return(quants_unshuffled?quantize_fns[i]:quantize_fns_v2[i]);
}
@ -3895,7 +3896,14 @@ struct ggml_v2_context * ggml_v2_init(struct ggml_v2_init_params params) {
}
#if defined(GGML_USE_CUBLAS)
ggml_v2_init_cublas();
if(quants_unshuffled)
{
ggml_v2_init_cublas();
}
else
{
ggml_v2_init_cublas_legacy();
}
#elif defined(GGML_USE_CLBLAST)
if(quants_unshuffled)
{
@ -9451,7 +9459,13 @@ static void ggml_v2_compute_forward_mul_mat_f32(
#if defined(GGML_USE_CUBLAS)
if (ggml_v2_cuda_can_mul_mat(src0, src1, dst)) {
if (params->ith == 0 && params->type == GGML_V2_TASK_COMPUTE) {
if(quants_unshuffled)
{
ggml_v2_cuda_mul_mat(src0, src1, dst, params->wdata, params->wsize);
}else
{
ggml_v2_cuda_mul_mat_legacy(src0, src1, dst, params->wdata, params->wsize);
}
}
return;
}
@ -9645,7 +9659,13 @@ static void ggml_v2_compute_forward_mul_mat_f16_f32(
#if defined(GGML_USE_CUBLAS)
if (ggml_v2_cuda_can_mul_mat(src0, src1, dst)) {
if (params->ith == 0 && params->type == GGML_V2_TASK_COMPUTE) {
if(quants_unshuffled)
{
ggml_v2_cuda_mul_mat(src0, src1, dst, params->wdata, params->wsize);
}else
{
ggml_v2_cuda_mul_mat_legacy(src0, src1, dst, params->wdata, params->wsize);
}
}
return;
}
@ -9884,7 +9904,13 @@ static void ggml_v2_compute_forward_mul_mat_q_f32(
#if defined(GGML_USE_CUBLAS)
if (ggml_v2_cuda_can_mul_mat(src0, src1, dst)) {
if (params->ith == 0 && params->type == GGML_V2_TASK_COMPUTE) {
if(quants_unshuffled)
{
ggml_v2_cuda_mul_mat(src0, src1, dst, params->wdata, params->wsize);
}else
{
ggml_v2_cuda_mul_mat_legacy(src0, src1, dst, params->wdata, params->wsize);
}
}
return;
}

17
otherarch/gpt2_v3.cpp
View file

@ -16,7 +16,9 @@
#include "model_adapter.h"
#if defined(GGML_USE_CLBLAST)
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
#elif defined(GGML_USE_CLBLAST)
#include "ggml-opencl.h"
#endif
@ -349,25 +351,32 @@ ModelLoadResult gpt2_model_load(const std::string & fname, gpt2_model & model, g
fin.close();
//gpu offload
#if defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CLBLAST) || defined(GGML_USE_CUBLAS)
if(gpulayers>0)
{
const auto & hparams = model.hparams;
size_t vram_total = 0;
const int n_gpu = std::min(gpulayers, int(hparams.n_layer));
fprintf(stderr, "%s: [opencl] offloading %d layers to GPU\n", __func__, n_gpu);
fprintf(stderr, "%s: [GPU] offloading %d layers to GPU\n", __func__, n_gpu);
for (int i = 0; i < n_gpu; ++i) {
const auto & layer = model.layers[i];
layer.c_attn_attn_w->backend = GGML_BACKEND_GPU;
layer.c_attn_proj_w->backend = GGML_BACKEND_GPU;
layer.c_mlp_fc_w->backend = GGML_BACKEND_GPU;
layer.c_mlp_proj_w->backend = GGML_BACKEND_GPU;
#if defined(GGML_USE_CLBLAST)
ggml_cl_transform_tensor(layer.c_attn_attn_w->data,layer.c_attn_attn_w); vram_total += ggml_nbytes(layer.c_attn_attn_w);
ggml_cl_transform_tensor(layer.c_attn_proj_w->data,layer.c_attn_proj_w); vram_total += ggml_nbytes(layer.c_attn_proj_w);
ggml_cl_transform_tensor(layer.c_mlp_fc_w->data,layer.c_mlp_fc_w); vram_total += ggml_nbytes(layer.c_mlp_fc_w);
ggml_cl_transform_tensor(layer.c_mlp_proj_w->data,layer.c_mlp_proj_w); vram_total += ggml_nbytes(layer.c_mlp_proj_w);
#else
ggml_cuda_transform_tensor(layer.c_attn_attn_w->data,layer.c_attn_attn_w); vram_total += ggml_nbytes(layer.c_attn_attn_w);
ggml_cuda_transform_tensor(layer.c_attn_proj_w->data,layer.c_attn_proj_w); vram_total += ggml_nbytes(layer.c_attn_proj_w);
ggml_cuda_transform_tensor(layer.c_mlp_fc_w->data,layer.c_mlp_fc_w); vram_total += ggml_nbytes(layer.c_mlp_fc_w);
ggml_cuda_transform_tensor(layer.c_mlp_proj_w->data,layer.c_mlp_proj_w); vram_total += ggml_nbytes(layer.c_mlp_proj_w);
#endif
}
fprintf(stderr, "%s: [opencl] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
fprintf(stderr, "%s: [GPU] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
}
#endif

15
otherarch/gptj_v3.cpp
View file

@ -16,7 +16,9 @@
#include "model_adapter.h"
#if defined(GGML_USE_CLBLAST)
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
#elif defined(GGML_USE_CLBLAST)
#include "ggml-opencl.h"
#endif
@ -337,7 +339,7 @@ ModelLoadResult gptj_model_load(const std::string & fname, gptj_model & model, g
fin.close();
//gpu offload
#if defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CLBLAST) || defined(GGML_USE_CUBLAS)
if(gpulayers>0)
{
const auto & hparams = model.hparams;
@ -352,12 +354,21 @@ ModelLoadResult gptj_model_load(const std::string & fname, gptj_model & model, g
layer.c_attn_proj_w->backend = GGML_BACKEND_GPU;
layer.c_mlp_fc_w->backend = GGML_BACKEND_GPU;
layer.c_mlp_proj_w->backend = GGML_BACKEND_GPU;
#if defined(GGML_USE_CLBLAST)
ggml_cl_transform_tensor(layer.c_attn_q_proj_w->data,layer.c_attn_q_proj_w); vram_total += ggml_nbytes(layer.c_attn_q_proj_w);
ggml_cl_transform_tensor(layer.c_attn_k_proj_w->data,layer.c_attn_k_proj_w); vram_total += ggml_nbytes(layer.c_attn_k_proj_w);
ggml_cl_transform_tensor(layer.c_attn_v_proj_w->data,layer.c_attn_v_proj_w); vram_total += ggml_nbytes(layer.c_attn_v_proj_w);
ggml_cl_transform_tensor(layer.c_attn_proj_w->data,layer.c_attn_proj_w); vram_total += ggml_nbytes(layer.c_attn_proj_w);
ggml_cl_transform_tensor(layer.c_mlp_fc_w->data,layer.c_mlp_fc_w); vram_total += ggml_nbytes(layer.c_mlp_fc_w);
ggml_cl_transform_tensor(layer.c_mlp_proj_w->data,layer.c_mlp_proj_w); vram_total += ggml_nbytes(layer.c_mlp_proj_w);
#else
ggml_cuda_transform_tensor(layer.c_attn_q_proj_w->data,layer.c_attn_q_proj_w); vram_total += ggml_nbytes(layer.c_attn_q_proj_w);
ggml_cuda_transform_tensor(layer.c_attn_k_proj_w->data,layer.c_attn_k_proj_w); vram_total += ggml_nbytes(layer.c_attn_k_proj_w);
ggml_cuda_transform_tensor(layer.c_attn_v_proj_w->data,layer.c_attn_v_proj_w); vram_total += ggml_nbytes(layer.c_attn_v_proj_w);
ggml_cuda_transform_tensor(layer.c_attn_proj_w->data,layer.c_attn_proj_w); vram_total += ggml_nbytes(layer.c_attn_proj_w);
ggml_cuda_transform_tensor(layer.c_mlp_fc_w->data,layer.c_mlp_fc_w); vram_total += ggml_nbytes(layer.c_mlp_fc_w);
ggml_cuda_transform_tensor(layer.c_mlp_proj_w->data,layer.c_mlp_proj_w); vram_total += ggml_nbytes(layer.c_mlp_proj_w);
#endif
}
fprintf(stderr, "%s: [opencl] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
}

13
otherarch/mpt_v3.cpp
View file

@ -16,7 +16,9 @@
#include "model_adapter.h"
#if defined(GGML_USE_CLBLAST)
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
#elif defined(GGML_USE_CLBLAST)
#include "ggml-opencl.h"
#endif
@ -292,7 +294,7 @@ bool mpt_model_load(const std::string & fname, mpt_model & model, gpt_vocab & vo
fin.close();
//gpu offload
#if defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CLBLAST) || defined(GGML_USE_CUBLAS)
if(gpulayers>0)
{
const auto & hparams = model.hparams;
@ -305,10 +307,17 @@ bool mpt_model_load(const std::string & fname, mpt_model & model, gpt_vocab & vo
layer.ffn_down_proj->backend = GGML_BACKEND_GPU;
layer.c_attn_wqkv_weight->backend = GGML_BACKEND_GPU;
layer.c_attn_out_proj_weight->backend = GGML_BACKEND_GPU;
#if defined(GGML_USE_CLBLAST)
ggml_cl_transform_tensor(layer.ffn_up_proj->data,layer.ffn_up_proj); vram_total += ggml_nbytes(layer.ffn_up_proj);
ggml_cl_transform_tensor(layer.ffn_down_proj->data,layer.ffn_down_proj); vram_total += ggml_nbytes(layer.ffn_down_proj);
ggml_cl_transform_tensor(layer.c_attn_wqkv_weight->data,layer.c_attn_wqkv_weight); vram_total += ggml_nbytes(layer.c_attn_wqkv_weight);
ggml_cl_transform_tensor(layer.c_attn_out_proj_weight->data,layer.c_attn_out_proj_weight); vram_total += ggml_nbytes(layer.c_attn_out_proj_weight);
#else
ggml_cuda_transform_tensor(layer.ffn_up_proj->data,layer.ffn_up_proj); vram_total += ggml_nbytes(layer.ffn_up_proj);
ggml_cuda_transform_tensor(layer.ffn_down_proj->data,layer.ffn_down_proj); vram_total += ggml_nbytes(layer.ffn_down_proj);
ggml_cuda_transform_tensor(layer.c_attn_wqkv_weight->data,layer.c_attn_wqkv_weight); vram_total += ggml_nbytes(layer.c_attn_wqkv_weight);
ggml_cuda_transform_tensor(layer.c_attn_out_proj_weight->data,layer.c_attn_out_proj_weight); vram_total += ggml_nbytes(layer.c_attn_out_proj_weight);
#endif
}
fprintf(stderr, "%s: [opencl] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
}

13
otherarch/neox_v3.cpp
View file

@ -14,7 +14,9 @@
#include <iostream>
#include <algorithm>
#if defined(GGML_USE_CLBLAST)
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
#elif defined(GGML_USE_CLBLAST)
#include "ggml-opencl.h"
#endif
@ -324,7 +326,7 @@ ModelLoadResult gpt_neox_model_load(const std::string & fname, gpt_neox_model &
fin.close();
//gpu offload
#if defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CLBLAST) || defined(GGML_USE_CUBLAS)
if(gpulayers>0)
{
const auto & hparams = model.hparams;
@ -337,10 +339,17 @@ ModelLoadResult gpt_neox_model_load(const std::string & fname, gpt_neox_model &
layer.c_attn_proj_w->backend = GGML_BACKEND_GPU;
layer.c_mlp_fc_w->backend = GGML_BACKEND_GPU;
layer.c_mlp_proj_w->backend = GGML_BACKEND_GPU;
#if defined(GGML_USE_CLBLAST)
ggml_cl_transform_tensor(layer.c_attn_attn_w->data,layer.c_attn_attn_w); vram_total += ggml_nbytes(layer.c_attn_attn_w);
ggml_cl_transform_tensor(layer.c_attn_proj_w->data,layer.c_attn_proj_w); vram_total += ggml_nbytes(layer.c_attn_proj_w);
ggml_cl_transform_tensor(layer.c_mlp_fc_w->data,layer.c_mlp_fc_w); vram_total += ggml_nbytes(layer.c_mlp_fc_w);
ggml_cl_transform_tensor(layer.c_mlp_proj_w->data,layer.c_mlp_proj_w); vram_total += ggml_nbytes(layer.c_mlp_proj_w);
#else
ggml_cuda_transform_tensor(layer.c_attn_attn_w->data,layer.c_attn_attn_w); vram_total += ggml_nbytes(layer.c_attn_attn_w);
ggml_cuda_transform_tensor(layer.c_attn_proj_w->data,layer.c_attn_proj_w); vram_total += ggml_nbytes(layer.c_attn_proj_w);
ggml_cuda_transform_tensor(layer.c_mlp_fc_w->data,layer.c_mlp_fc_w); vram_total += ggml_nbytes(layer.c_mlp_fc_w);
ggml_cuda_transform_tensor(layer.c_mlp_proj_w->data,layer.c_mlp_proj_w); vram_total += ggml_nbytes(layer.c_mlp_proj_w);
#endif
}
fprintf(stderr, "%s: [opencl] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
}